Inpatient outcomes following admission to stabilization-focused complex trauma- and dissociation-specific unit.
Bibliographic record
Abstract
OBJECTIVE: Individuals with histories of complex psychological trauma, such as those with dissociative disorders, are often high utilizers of inpatient services and thus warrant further research attention. The present study sought to examine whether treatment on a specialized inpatient trauma unit was associated with improvements in adaptive functioning, emotion regulation, and dissociation among patients experiencing complex posttraumatic and dissociative symptoms. METHOD: = 54), the majority of whom had a dissociative disorder, at a specialized trauma disorders unit to analyze differences in scores between intake and discharge on measures of adaptive functioning, emotion regulation, and dissociation. RESULTS: = .30, in dissociative absorption. CONCLUSIONS: Our findings suggest that inpatient treatment modeled after expert consensus treatment guidelines is associated with significant improvements in adaptive functioning and emotion regulation and reduced dissociative absorption in individuals experiencing severe and acute complex posttraumatic and dissociative symptoms. Appropriate screening and symptom-specific treatment of complex posttraumatic and dissociative symptoms are recommended to improve outcomes for this population during inpatient hospital admission. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".